Towards a pragmatist dealing with algorithmic bias in medical machine learning

Starke, Georg and De Clercq, Eva and Elger, Bernice S.. (2021) Towards a pragmatist dealing with algorithmic bias in medical machine learning. Medicine, Health Care and Philosophy, 24 (3). pp. 341-349.

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Official URL: https://edoc.unibas.ch/83083/

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Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and erroneous treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James's pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine.
Faculties and Departments:08 Cross-disciplinary Subjects > Ethik > Institut für Bio- und Medizinethik > Bio- und Medizinethik (Elger)
03 Faculty of Medicine > Departement Public Health > Ethik in der Medizin > Bio- und Medizinethik (Elger)
UniBasel Contributors:Starke, Georg and De Clercq, Eva and Elger, Bernice Simone
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Kluwer Academic
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:31 Aug 2021 08:48
Deposited On:31 Aug 2021 08:48

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